Prediction of seasonal snow accumulation in cold climate forests
Bibliographic record
Abstract
Abstract Accumulation of snow under forest canopies is known to decline with increasing canopy density and leaf area because of snow interception and sublimation in the canopy. Seasonal snow accumulation measurements, collected over a decade from various forest stands in western Canada, were used to test and develop methods to relate forest snow accumulation to stand properties and observations of either small‐clearing seasonal snow accumulation or seasonal snowfall. At sub‐stand scales, the variability of seasonal snow accumulation was not well related to stand leaf area, seasonal interception or small‐clearing seasonal snow accumulation. At the stand scale, physically based snow interception equations predicted seasonal snow accumulation from the stand leaf area and the seasonal snow accumulation or snowfall in adjacent clearings. A simple parametric form of these equations showed the sensitivity of seasonal snow accumulation to leaf area at the forest stand scale and suggested a relationship to extrapolate snow accumulation or snowfall measurements from clearings to forests. These relationships, developed from Canadian boreal forest observations, are consistent with Kuz'min's (1960. Formirovanie Snezhnogo Pokrova i Metody Opredeleniya Snegozapasov . Gidrometeoizdat: Leningrad) relationship between accumulation and canopy density derived from Russian observations, suggesting a good degree of transferability. Copyright © 2002 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".